Economic Complexity and the Green Economy∗

Penny Mealy† Alexander Teytelboym‡

Abstract

Which countries currently have the productive capabilities to thrive inthe green economy? How might countries reorient their existing industrialstructures to be more competitive in an environmentally friendly world?To investigate these questions, this paper develops a novel methodology formeasuring productive capabilities to the green economy. By constructinga new and comprehensive dataset of traded green products and drawingon economic complexity methods, we rank countries in terms of their abil-ity to export complex green products competitively. We show that higherranked countries are more likely to have higher environmental patentingrates, lower CO2 emissions, and more stringent environmental policies evenafter controlling for per capita GDP. We then examine countries’ potentialto transition into green products in the future and find strong path de-pendence in the accumulation of green capabilities. Our results shed newlight on green industrialisation and have a number of implications for greenindustrial policy.

∗This project was supported by Partners for a New Economy and the Oxford Martin SchoolProgramme on the Post-Carbon Transition. We would also like to thank Simon Angus, DianeCoyle, Neave O’Clery, Cameron Hepburn, Cesar Hidalgo, Helen Schweiger, Rick van der Ploeg,and seminar participants at the Oxford OxCarre seminar for helpful conversations and feedback.First draft: September, 2017†Email: penny.mealy@inet.ox.ac.uk. Institute for New Economic Thinking at the Oxford

Martin School, and the Smith School for Enterprise and the Environment, University of Oxford.‡Email: alexander.teytelboym@economics.ox.ac.uk. Department of Economics, St.

Catherine’s College, Institute for New Economic Thinking at the Oxford Martin School, andthe Smith School for Enterprise and the Environment, University of Oxford.

1

1 Introduction

The transition to the green economy is high on policymakers’ agendas and is likely

to involve a transformation of economic activities around the world (World Bank,

2012; AfDB, 2013; ADB, 2013; EBRD, 2017). With greater emphasis placed on

clean technologies and environmentally friendly goods and services, the shift to a

greener growth model has the potential to alter the global competitive landscape

(Fankhauser et al., 2013). Such structural changes are likely to have important

implications for the economic fortunes of nations.

Research in economic complexity has shown that the mix of products in coun-

tries’ export baskets influences their future economic diversification possibilities

and growth outcomes (Hidalgo et al., 2007; Hausmann et al., 2007; Hidalgo and

Hausmann, 2009; Hausmann et al., 2014). However, relatively little work has stud-

ied the relevance of countries’ productive structures for their success in the green

economy. A key barrier has been a lack of a universally accepted definition of

environmental goods and services. In 2001, the World Trade Organisation (WTO)

instigated a mandate to reduce or eliminate tariffs on environmental goods and

services and numerous lists of green products have since been proposed by various

international organisations (WTO, 2001). However, largely due to the conceptual

and practical challenges of identifying and classifying products with environmental

benefits, a global consensus on green goods and services has not yet been reached

(Bucher et al., 2014).1

This paper first addresses the empirical gap by pooling together all existing en-

vironmental goods classifications from the WTO, the Organisation for Economic

Cooperation and Development (OECD), and the Asia-Pacific Economic Coopera-

tion (APEC) into a comprehensive dataset of green products traded between 1995

and 2014. We then extend methodologies from the economic complexity litera-

ture to examine: (i) which countries are currently best placed to benefit from the

transition to the green economy, and (ii) how countries might reorient their indus-

trial structure to become more competitive in green (or environmentally friendly)

1The latest round of talks on WTO Environmental Goods Agreement which promised todeliver a list of green products stalled in December 2016.

2

products in the future.

We start by showing that compared to all traded products, green products have, on

average, higher Product Complexity Index (PCI) values (Hidalgo and Hausmann,

2009). The PCI has often been used as a proxy for the technological sophistication

of products (Felipe et al., 2012; Poncet and de Waldemar, 2013; Javorcik et al.,

2018). Our finding suggests that green products, on average, require more tech-

nologically advanced know-how than non-green products (see also Dechezlepretre

et al. (2014)).

In order to understand which countries are more likely to thrive in the green

economy, we develop a new measure called the Green Complexity Index (GCI). The

GCI aims to estimate a country’s green production capabilities. A country’s GCI is

increasing in the number and the PCI of green products can export competitively.2

Top ranked countries in 2014 include Germany, Italy, the United States, Austria,

and Denmark, while countries such as Turkmenistan, Mauritania, and Angola

occupy the bottom ranks.

While the GCI is correlated with per capita GDP, it also captures a lot of varia-

tion in different environmentally relevant variables. For example, controlling for

countries’ per capita GDP, countries with higher GCI tend to have significantly

higher environmental patenting rates, lower CO2 emissions, and more stringent en-

vironmental policies. We also investigate the evolution of countries’ GCI rankings

between 1995 and 2014. We find relatively little variation in the highest rankings

(Germany has remained in the top position over the 20 year period). However,

other countries, such as China, Vietnam, and Uganda have made significant gains

in their GCI scores, while other countries such as Australia show notable declines.

To understand how countries may reorient their industrial structure to become

more competitive in green products, we draw on the “principle of relatedness”

from economic geography and economic complexity literatures (Hidalgo et al.,

2018). There is substantial evidence that countries are more likely to diversify

into products or industries that require related (or similar) production capabilities

2We say that a country is competitive in a product if its revealed comparative advantage(RCA) for this product is greater than 1 (Balassa, 1965). See Equation 1 below.

3

to those they currently possess (Patel and Pavitt, 1997; Weitzman, 1998; Hidalgo

et al., 2007; Neffke et al., 2011; Boschma et al., 2013). By assuming that the

transition to green or environmentally friendly products should follow the same

pattern, we apply relatedness measures developed in Hidalgo et al. (2007) to con-

struct each country’s Green Adjacent Possible (GAP). The GAP identifies the set

of green products that are most related (or similar) to a country’s current pro-

duction capabilities – i.e. the new green industrial opportunities that a country is

most likely to transition to given the country’s current technological capabilities.

We also develop the Green Complexity Potential (GCP) measure, which aggre-

gates the information contained in each country’s GAP into a single, comparable

metric. The GCP measures each country’s average relatedness to complex green

products that the country is not yet competitive in. We show that the GCP is able

to significantly predict future increases in a country’s GCI, green export share, and

the number of green products that a country is competitive in, even after control-

ling for each country’s GDP per capita. We also find a strong positive correlation

between the GCP and GCI suggesting that countries that currently export a sig-

nificant number of green complex products are generally well placed to diversify

into other green complex products in the future.

Our results contribute to a number of policy discussions. First, we establish an

extensive set of products relevant to the green economy and identify which coun-

tries currently have the capabilities to produce them competitively. Our findings

complement Fankhauser et al.’s (2013) related efforts to analyse “who will win

the green race”, but provides a broader coverage of countries and an alternative

analytical lens based on the economic complexity methodology. Second, our GCI

measure allows policymakers to assess a country’s green production capabilities

relative to other countries and also consider how its green competitiveness has

been changing over time. The path dependence in green diversification suggests

that earlier and more aggressive action to establish green production capabilities

is required in order to succeed in the future green economy (Aghion et al., 2014,

2016). Third, by identifying the GAPs we provide a data-driven indication of

which products countries are best placed to gain a competitive edge in, informing

policymakers about the likely directions of green diversification and as well as the

4

appropriate levers for green industrial policy (Aghion et al., 2011; Huberty and

Zachmann, 2011; Hallegatte et al., 2012, 2013; Rodrik, 2014). We also present

some preliminary results on the effect of recent green stimulus policies on green

exports and the GCI for a selection of countries.

This paper is organized as follows. Section 2 reviews background literature on

capabilities, diversification, and existing efforts to apply economic complexity ap-

proaches to study the green economy. Section 3 gives an overview of the data and

methods used in this paper. Section 4 presents our results and Section 5 discusses

key policy implications and avenues for future work. The Appendix contains more

information about the data (A.1 and A.2), green products and their relatedness

(A.4 and A.6), countries and green exports (A.3 and A.5) and also gives further

regression robustness checks (A.7).

2 Related literature

2.1 Capabilities and complexity

The notion of “production capabilities” has strong ties to the growth and develop-

ment literature. In the development context, capabilities are often discussed with

reference to the technologies, productive know-how, infrastructure, and institu-

tions that enable a country to improve its productivity and achieve higher growth

rates (Lall, 1992; Bell and Pavitt, 1995; Sutton and Trefler, 2016). In this paper,

we consider production capabilities in a similar spirit, but with a distinct focus on

the set of capabilities that are relevant to the green economy.

However, precisely defining and measuring “productive capabilities” is challenging.

A number of efforts have tried to infer information about countries’ productive ca-

pabilities using trade data, with the key assumption that if a country has revealed

comparative advantage in a product, then it must have the capabilities to produce

it competitively (Lall, 2000; Lall et al., 2006; Hausmann et al., 2007; Hidalgo and

Hausmann, 2009; Hausmann et al., 2014). Trade data is also advantageous in of-

fering a rich source of detailed information on tradable goods comparable across

5

time and space.

Strategies to measure capabilities relevant for growth and development have taken

various forms (see Verspagen et al. (2015) for a review). Here we focus on the

country-based Economic Complexity Index (ECI) and product-based Product Com-

plexity Index (PCI), which were originally introduced in Hidalgo and Hausmann

(2009) in order to infer the “complexity” (or technological sophistication) of coun-

tries’ production capabilities. The ECI and PCI have a number of different eco-

nomically relevant mathematical interpretations including spectral clustering, dif-

fusion maps, and correspondence analysis (Mealy et al., 2019).

The ECI has attracted significant attention from researchers and policymakers

because it can explain more variation in country income per capita and economic

growth than other variables commonly employed in growth regressions such as gov-

ernance, institutional quality, education, and competitiveness (Hidalgo and Haus-

mann, 2009; Hausmann et al., 2014). As we will discuss further in section 3.2,

when applied to trade data, the ECI provides a ranking of countries by exploiting

the pattern of similarities in their export portfolios. Countries with a high ECI

have export baskets that are similar to other countries with a high ECI, and these

countries tend to be advanced economies that are able to export technologically

sophisticated products competitively. In contrast, countries with low ECI have

export baskets that tend to be characterized by less technologically sophisticated

products (Mealy et al., 2019). The PCI, on the other hand, provides a similarity

ordering over products. High-PCI products, which are exported by high-ECI coun-

tries, tend to reflect more technologically sophisticated products, and vice versa

for low-PCI products.

Although this paper primarily focuses on the measures proposed by Hausmann

et al. (2014), we note that alternative measures for capturing the “complexity”

or “fitness” of countries’ productive capabilities have also been proposed by Tac-

chella et al. (2012). These measures are calculated as the fixed-point solution

of a non-linear mapping function, which instead exploits the pattern of export

diversity (the number of products a country is able to export competitively). Tac-

chella et al.’s (2012) Country Fitness measure (an alternative to the ECI) can be

6

thought of as a weighted-diversity measure, where each product that a country

exports competitively is weighted by its estimated “complexity”. Tacchella et al.’s

(2012) corresponding Product Complexity measure is a non-linear function that

is inversely related to the number of countries that can export a given product

competitively.

2.2 Relatedness and diversification

The tendency for countries and regions to diversify into economic activities that

involve related production capabilities to activities they already specialize in has

received significant attention in the economic geography literature (see Hidalgo

et al. (2018) for an overview). The underlying intuition is that if a country or region

has the capabilities to produce shirts, it is relatively easy for it to diversify into the

production of trousers because many of the requisite production capabilities (such

as sewing techniques, factory layout, textile supply chains, clothing designs) are

similar. However, it is more difficult for that country or region to diversify directly

from producing shirts to trucks because it would need to acquire a large amount

of new production know-how and invest in completely new factors of production

(Hausmann et al., 2014).

Evidence to support this “stickiness” in the knowledge accumulation process has

been documented using a range of different data sources for a variety of activities.

For example, Hidalgo et al. (2007) measured the relatedness between exported

products by examining their probability of being co-exported and found this mea-

sure to be significantly predictive of future export diversification. Boschma et al.

(2013) applied a similar approach to investigate regional diversification in Spain.

Alternative strategies have measured relatedness by studying the flow of workers

between industries (Neffke and Henning, 2013; O’Clery et al., 2016; Neffke et al.,

2017) and firms (Guerrero and Axtell, 2013), or by looking at the strength of

input-output linkages across industries (Essletzbichler, 2015). Research has also

investigated relatedness underpinning different technologies by investigating patent

citations (Leten et al., 2007; Rigby, 2015) and the co-classification of patents across

technology classes (Kogler et al., 2013, 2017).

7

In addition to complementing the broader literature on the path-dependent na-

ture of economic development (David, 1985; Arthur, 1989; Krugman, 1991a,b,c;

Matsuyama, 1991; Arthur, 1994; David, 1994; Aghion et al., 2014, 2016), efforts

to measure and understand related diversification has also provided policymakers

with new frameworks to analyze industrial development policies (Boschma and

Gianelle, 2014; Thissen et al., 2013; Balland et al., 2018). But despite calls from

policymakers and development agencies to find greener development strategies (e.g.

Lin and Xu (2014); Hamdok (2015); Brahmbhatt et al. (2017); Newfarmer et al.

(2018)), there has been relatively limited efforts to apply notions of relatedness to

better navigate the transition to the green economy.

2.3 Applications of economic complexity to the green econ-

omy

Although limited data and the lack of a universally accepted definition of envi-

ronmental goods and services has hampered efforts to study the transition to the

green economy from an economic complexity perspective, some recent work has

made fruitful progress in this direction. In particular, Fankhauser et al. (2013)

investigated countries’ “green competitiveness” by drawing on data on sectoral

patenting rates, exports, and industry output. However, due to data limitations,

this work only examined 8 countries in 110 manufacturing sectors. Inspired by

the methodological approaches underpinning the Product Space (Hidalgo et al.,

2007), Hamwey et al. (2013) identified 11 green products in the Product Space fo-

cusing on the case of Brazil. Huberty and Zachmann (2011) looked at the position

of 6 green products in the Product Space (relating to electric meters, solar cells,

wind turbines, and nuclear reactors) to analyze the effectiveness of green industrial

policies in the context of European countries. Fraccascia et al. (2018) analysed 41

green products in 141 countries and showed that green products with the high-

est growth potential tend to be products that are the most related to countries’

existing export structures.

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3 Data and Methods

3.1 Green trade data

To construct the dataset of green products used in this paper, we draw on existing

lists and classifications developed by the World Trade Organization (WTO, 2010,

2011), the Organisation for Economic Cooperation and Development (OECD,

1999; Sauvage, 2014), and the Asia-Pacific Economic Cooperation (APEC, 2012)

(see Table 7 in Appendix A.1). We combine all available lists to construct a dataset

totalling 543 products classified at the 6-digit level in HS1992. We then combine

this dataset with COMTRADE data to analyse environmental trade across coun-

tries for the period 1995-2014.

While our dataset of 543 products represents a useful benchmark of potentially

green products, the environmental status of a number of products included the

broad-reaching WTO Reference List may be questionable.3 In order to arrive

at a robust set of green products that share wide expert endorsement—and are

useful to policymakers—we develop two main product lists. The first is a list

of 293 green products, which we obtain by taking the union of the WTO Core

list, OECD list, and the APEC list.4 This refined list of green products has the

advantage that each product has either been endorsed by a large number of WTO

or APEC member countries, or its environmental benefits have been determined

by the (rather selective) OECD. This list represents a range of environmental

categories, such as air pollution, waste water management, and recycling. We use

this green product list for our empirical analysis throughout the paper.

We also develop a smaller list of 57 renewable energy products (a subset of the

products on the green product list). This list includes all products falling under the

WTO Renewable Energy Products category, under the OECD’s Renewable En-

ergy Plant categories, as well as two additional APEC renewable energy products

(solar heliostats and parts for solar heliostats) that were not included on either the

3For example 848210 (ball bearings) submitted by Saudi Arabia with the rationale that theyare used in carbon capture and storage applications.

4While the original set of green products included 295 goods, we had to remove Profile Pro-jectors (903110) and Exposure meters (902740) due to data quality issues.

9

WTO or OECD lists. The renewable energy product list focuses on low-carbon

technologies that are key for addressing climate change. More information about

the green product data can be found in Appendix A.1 and A.2. All our data are

available upon request.

3.2 Economic Complexity Index and Product Complexity

Index

We first calculate the ECI and PCI for COMTRADE export data. Here, we follow

the approach set out in Hausmann et al. (2014) and define a binary country-

product matrix M , with elements Mcp indexed by country c and product p. Mcp =

1 if country c has a ‘revealed comparative advantage’ (RCA) > 1 in product p and

0 otherwise. RCA is calculated using the Balassa (1965) index

RCAcp =xcp/

∑p xcp∑

c xcp/∑

c

∑p xcp

, (1)

where xcp is country c’s exports of product p.

We can calculate how many products a country has RCA in (its diversity) by

summing across the rows of the M matrix (denoted dc). Similarly, we can count

how many countries have RCA in a given product (its ubiquity) by summing aross

the columns of the M matrix (denoted up). That is,

dc =∑p

Mcp (2)

and

up =∑c

Mcp. (3)

The ECI is defined as the eigenvector associated with the second largest eigenvalue

of the matrix

M = D−1S, (4)

10

where D is the diagonal matrix formed from the diversity vector, and S is a matrix

whose rows and columns correspond to countries and whose entries are given by

Scc′ =∑p

McpMc′p

up. (5)

S is a symmetric similarity matrix, which corresponds to how similar two countries’

exports baskets are.

The associated PCI measure is symmetrically defined as the eigenvector associated

with the second largest eigenvalue of the transpose of the M matrix.

3.3 Green Complexity Index

Our Green Complexity Index (GCI) draws on the PCI measure described above.

It aims to capture the extent to which countries can competitively export a diverse

range of technologically sophisticated green products and is given by

GCIc =∑g

ρgPCIg. (6)

Here ρg is a binary vector in which a 1 corresponds to a country having RCA > 1

in green product g and 0 otherwise, and PCIg is the Product Complexity Index

of g normalized to take a value between 0 and 1.

It is important to emphasize how a country’s GCI differs from its ECI. While the

ECI represents the average PCI of all products a country is competitive in, the

GCI sums up the PCI of green products a country is competitive in. Note that

while we have applied the GCI to a specific subset of green traded products, the

measure is completely general and can be applied to any subset of products (such

as biotech).

11

3.4 Product proximity and Product density

In order to estimate how related two products are in terms of their underpinning

production capabilities, we draw on Hidalgo et al.’s (2007) measure of product

proximity (denoted φij), which is increasing in the likelihood that two products i

and j are exported by the same country.

φij = min(P(RCAi > 1|RCAj > 1),P(RCAj > 1|RCAi > 1)). (7)

Here, P(RCAi > 1|RCAj > 1) is the conditional probability that a country is

competitive in product i given that it is competitive in product j. Following

Hidalgo et al. (2007), we take the minimum to ensure that φij = φji.

To estimate how related a given product is to a country’s current set of production

capabilities, we employ a second measure introduced in Hidalgo et al. (2007) known

as density. This measure (denoted ωcj) calculates the average proximity between a

given product j and all the products country c can currently export competitively

and is given by

ωcj =

∑i ρiφij∑i φij

, (8)

where ρi is a vector of i products for which country c has RCA > 1.

3.5 Green Complexity Potential

Finally, we introduce a measure we call Green Complexity Potential (GCP). This

measure operates on green products that countries are not presently competitive

in, and, as the name suggests, aims to estimate how much “potential” countries

have to diversify into green, technologically sophisticated products in the future.

The GCP for country c is given by

GCPc =1

|1− ρg|∑

(1− ρg)ωcgPCIg, (9)

12

where 1 − ρg is the vector of green products a country currently does not have

RCA > 1 in, ωcg is the proximity of product g to country c, and PCIg is the PCI

of product g, normalized to take a value between 0 and 1. GCP is similar to the

Complexity Outlook Index (Hausmann et al., 2014) and the Complexity Potential

measure (O’Clery et al., 2016). However, while these measures are applied to the

entire set of traded products (Hausmann et al., 2014) or industries (O’Clery et al.,

2016), the GCP is specific to the subset of green products.

4 Results

4.1 Trade in green and renewable products

Before turning to our results on green production capabilities, we first look at

the total volume of trade green and renewable products represent and how this

has changed over time. As shown in Panel A of Figure 1, green and renewable

energy products have exhibited steady growth in trade volumes, particularly over

the 2000—2011 period, with a levelling off in later years. As of 2014, total trade

in green products was around $1.5 trillion, while trade in renewable products was

around $0.5 trillion.

13

1995 2000 2005 2010 20140.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

$U

S T

rilli

ons

A

Green Products

Renewable Energy Products

1995 2000 2005 2010 20140.00

0.02

0.04

0.06

0.08

0.10

0.12

Pro

port

ion o

f Tota

l Tra

de

B

Green Products

Renewable Energy Products

Figure 1: Growth in Green and Renewable Energy Products

Interestingly, Panel B shows that when we examine the evolution of trade in green

and renewable products as a proportion of total trade, the trajectory has been

relatively flat. Over the 20 year period, green products has accounted for around

8.5% of global trade, while renewable energy products has fluctuated around 3%.

In Appendix A.3, we present some further results showing the top exporters of

green and renewable products (in terms of trade volumes).

4.2 PCI of green and renewable products

In Table 1 and 2 we present the top 10 and bottom 10 green products ranked ac-

cording to their PCI values for the year 2014. We also include the environmental

benefit or category associated with each product. Similar tables for renewable en-

ergy products in are presented in Appendix A.4. Green products with the highest

PCI values relate to devices used for environmental monitoring and analysis, and

concentrated solar technologies, green products with the lowest PCI values tend

to relate to environmentally friendly products – many of which are made from

vegetable material.

14

Table 1: Top 10 Green Products by PCI

Rank PCI HS6 Code Product Description Environmental Benefits

1 2.5073 901380Optical devices, appliances and in-

struments, nes

Solar Heliostats (Heliostats orient mirrors in concen-

trated solar power systems to reflect sunlight on to a

CSP receiver)

2 2.0716 902790Microtomes, parts of scientific anal-

ysis equipment

Microtomes are devices that prepare slices of samples

for analysis - used in environmental monitoring

3 2.0134 847989Machines and mechanical appli-

ances, nes

Machines and appliances designed for a wide range of

areas of environmental management including waste,

waste water, drinking water production and soil re-

mediation

4 1.8805 902730Spectrometers, spectrophotome-

ters, etc using light

Used in a wide range of environmental applications,

including identification of unknown chemicals, tox-

ins and trace contaminants, environmental control,

water management, food processing, agriculture and

weather monitoring

5 1.8625 902780Equipment for physical or chemical

analysis, nes

Used to measure, record, analyse and assess environ-

mental samples or environmental influences

6 1.8291 680690Mineral heat or sound insulating

materials and articlesUsed for heat and energy management

7 1.8119 902720Chromatographs, electrophoresis

instruments

Used to monitor and analyse air pollution emissions,

ambient air quality, water quality, etc.

8 1.8077 902710 Gas/smoke analysis apparatusUsed for monitoring and analysing environmental pol-

lution.

9 1.7945 847990Parts of machines and mechanical

appliances nes

Parts for environmental management devices (Ma-

chines and appliances designed for a wide range of

areas of environmental management including waste,

waste water, drinking water production and soil re-

mediation)

10 1.7795 848360Clutches, shaft couplings, universal

joints

Used for initial assembly, repair, and maintenance of

wind energy systems

Table 2: Bottom 10 Green Products by PCI

Rank PCI HS6 Code Product Description Environmental Benefits

284 −1.2445 871200Bicycles, other cycles, not motor-

ized

Cleaner or more resource efficient technology or prod-

uct

285 −1.2826 871411 Motorcycle SaddlesCleaner or more resource efficient technology or prod-

uct

286 −1.2935 220710Undenatured ethyl alcohol > 80%

by volumeRenewable Energy Plant

287 −1.4189 560790Twine, cordage, ropes and cables,

of other materialsEnvironmentally preferrable product

288 −1.5074 960310Brooms/brushes of vegetable mate-

rial

Waste collection equipment (solid waste manage-

ment)

289 −1.6088 560721Binder or baler twine, of sisal or

agaveEnvironmentally preferrable product

290 −1.864 460120Mats, matting and screens, veg-

etable plaiting materialEnvironmentally preferrable product

291 −2.1905 530599Vegetable fibre nes, processed not

spun, tow and wasteEnvironmentally preferrable product

292 −2.2365 630510Sacks and bags, packing, of jute or

other bast fibresEnvironmentally preferrable product

293 −2.9908 530310Jute and other textile bast fibres,

raw or processed but not spunEnvironmentally preferrable product

In Table 3, we show key descriptive statistics for the PCI of all HS6 products, green

15

products, and renewable energy products. Following Hausmann et al. (2014), we

have normalized the PCI values so that that the set of all HS1992 6 digit products

have a mean of 0 and standard deviation of 1.

Table 3: Product PCI distribution descriptive statistics

Product Set Number of Products Mean PCI Std PCI

All HS6 Products 4857 0 1

Green Products 293 0.48 0.79

Renewable Energy Products 57 0.49 0.72

We find that green and renewable energy products have higher PCI values than

average. Green products have a mean PCI of 0.48, while renewable energy products

have a mean PCI of 0.49. In Figure 2, we also show the distribution of PCI values

of green and renewable energy products and compare them to the distribution

of all traded products. To the extent that the PCI is an appropriate proxy for

measuring the technological sophistication of products, our results suggest that

green and renewable energy products, on average require more technologically

advanced know-how than typical products.5

5The Kolmogorov-Smirnov 2-sample tests reject the null hypothesis that green product PCIdistributions are different from all product PCI distributions (KS-Statistic for green productsvs all products = 0.242, p-value = 1.11 × 10−14). The Kolmogorov-Smirnov 2-sample test failsto reject the null hypothesis that the green and renewable energy products are drawn from thesame distribution (KS-Statistic = 0.096, p-value = 0.747)

16

6 4 2 0 2 4

Product Complexity Index (PCI)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

Pro

port

ion o

f Pro

duct

s

All products

Green products

Renewable energy products

Figure 2: PCI distribution for all HS6 products, green products and renewableenergy products

4.3 Green Complexity Index across countries and time

We now turn to the question of which countries have the most technologically

advanced, green production capabilities. Figure 3 presents the GCI ranks across

countries over the period 1995 (left axis) and 2014 (right axis). In 2014, Germany

held the top rank, followed by Italy, the United States, Austria, and Denmark.

The bottom ranks included countries such as Turkmenistan, Mauritania, Angola,

and Azerbaijan. Looking at how ranks have changed over the 20 year period,

Germany has impressively maintained its top position throughout. Some coun-

tries, such as China, Vietnam and Uganda have made significant gains in their

green production capabilities, while other countries, such as Australia, have seen

a substantial decline in their GCI rankings.

17

1995 2000 2005 2010 2014

Japan

SwitzerlandGermany

Sweden

United Kingdom

Czech Republic

South Korea

Finland

Austria

Singapore

United States

Slovenia

France

Slovakia

Hungary

Ireland

Italy

Israel

Denmark

NetherlandsPoland

ChinaMexico

Norway

Spain

Croatia

Belarus

Estonia

Thailand

Lithuania

Ukraine

Romania

Canada

Malaysia

Latvia

Hong Kong

Bulgaria

Russia

Portugal

Turkey

Bosnia and Herzegovina

Saudi Arabia

New Zealand

Panama

South Africa

United Arab Emirates

Lebanon

Greece

Philippines

IndiaJordan

Uruguay

Argentina

Colombia

Costa Rica

Brazil

Tunisia

Moldova

Kuwait

El Salvador

Macedonia

Georgia

Mauritius

Zambia

Chile

Egypt

Kyrgyzstan

Trinidad and Tobago

Australia

Indonesia

Vietnam

Jamaica

Albania

Guatemala

Uganda

Liberia

Paraguay

Dominican Republic

Kazakhstan

Oman

Qatar

Sri Lanka

Peru

Kenya

Honduras

Morocco

Pakistan

Uzbekistan

Zimbabwe

Iran

Bolivia

Ecuador

Nicaragua

Malawi

Tanzania

Cote dIvoire

Venezuela

Ghana

Republic of the Congo

Azerbaijan

Senegal

Mali

Cambodia

Madagascar

Yemen

Ethiopia

Mongolia

Tajikistan

Bangladesh

Laos

Sudan

Algeria

MozambiqueGabon

Angola

Cameroon

Nigeria

Papua New GuineaMauritania

Libya

Turkmenistan

Guinea

Japan

Switzerland

Germany

SwedenUnited Kingdom

Czech Republic

South Korea

Finland

Austria

Singapore

United States

Slovenia

France

Slovakia

Hungary

Ireland

Italy

Israel

Denmark

Netherlands

Poland

China

Mexico

Norway

Spain

Croatia

Belarus

Estonia

Thailand

Lithuania

Ukraine

Romania

Canada

MalaysiaLatvia

Hong Kong

Bulgaria

Russia

Portugal

Turkey

Bosnia and Herzegovina

Saudi Arabia

New Zealand

Panama

South Africa

United Arab Emirates

Lebanon

GreecePhilippines

India

Jordan

Uruguay

Argentina

Colombia

Costa Rica

Brazil

Tunisia

Moldova

Kuwait

El Salvador

Macedonia

Georgia

Mauritius

Zambia

Chile

Egypt

Kyrgyzstan

Trinidad and Tobago

Australia

Indonesia

Vietnam

Jamaica

Albania

Guatemala

Uganda

Liberia

Paraguay

Dominican Republic

Kazakhstan

Oman

Qatar

Sri Lanka

Peru

Kenya

HondurasMoroccoPakistan

Uzbekistan

Zimbabwe

Iran

Bolivia

Ecuador

Nicaragua

Malawi

Tanzania

Cote dIvoire

Venezuela

Ghana

Republic of the Congo

Azerbaijan

Senegal

Mali

Cambodia

Madagascar

Yemen

Ethiopia

Mongolia

Tajikistan

Bangladesh

Laos

Sudan

Algeria

Mozambique

Gabon

Angola

Cameroon

Nigeria

Papua New Guinea

Mauritania

Libya

Turkmenistan

Guinea

Figure 3: Green Complexity Index rankings over time

18

In Figure 4, we compare countries’ GCI to their ECI. Panel A shows a positive

correlation between the GCI and ECI values6, while Panel B shows the relationship

between country rankings.7 The positive correlations are unsurprising given that

richer countries tend to have higher ECI scores and green products tend to have

higher PCI on average.8 However, the deviations in the ranks and values of the

ECI and GCI are informative about the differences in the orientation of countries’

export baskets. For example, countries that are heavily focused on exporting oil

and petroleum products, such as Saudi Arabia, Trinidad and Tobago, and Qatar,

have lower GCI compared to their ECI. In contrast, Tunisia, China, and Italy have

much higher GCI scores relative to their ECI, suggesting that their production

capabilities may be more aligned to green products. It may also suggest that if a

green transition substantially increased demand for green goods, these countries

could stand to benefit, relative to other countries having similar ECI values.

2 1 0 1 2 3

Economic Complexity Index

1

0

1

2

3

Gre

en C

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ple

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y Index

Japan

Switzerland

Germany

SwedenUnited Kingdom

Czech Republic

South Korea

Finland

Austria

Singapore

United States

Slovenia

France

Slovakia

Hungary

Ireland

Italy

Israel

Denmark

Netherlands

Poland

China

Mexico

Norway

Spain

Croatia

Belarus

Estonia

Thailand

Lithuania

Ukraine

Romania

Canada

MalaysiaLatvia

Hong Kong

Bulgaria

Russia

Portugal

Turkey

Bosnia and Herzegovina

Saudi Arabia

New Zealand

Panama

South Africa

United Arab Emirates

Lebanon

GreecePhilippines

India

Jordan

Uruguay

ArgentinaColombiaCosta Rica

Brazil

Tunisia

Moldova

Kuwait

El SalvadorMacedoniaGeorgia

MauritiusZambiaChile

Egypt

KyrgyzstanTrinidad and Tobago

Australia

IndonesiaVietnam

JamaicaAlbania

GuatemalaUganda

LiberiaParaguay

Dominican Republic

KazakhstanOmanQatar

Sri Lanka

Peru

Kenya

HondurasMoroccoPakistanUzbekistanZimbabweIran

BoliviaEcuadorNicaragua

MalawiTanzania

Cote dIvoireVenezuelaGhanaRepublic of the Congo

Azerbaijan

Senegal

MaliCambodiaMadagascar YemenEthiopia

MongoliaTajikistanBangladeshLaos

Sudan AlgeriaMozambique

GabonAngola

Cameroon

NigeriaPapua New GuineaMauritaniaLibyaTurkmenistanGuinea

A

120 100 80 60 40 20 1

Economic Complexity Index Rank

120

100

80

60

40

20

1

Gre

en C

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ple

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y Index R

ank

Japan

Switzerland

Germany

SwedenUnited KingdomCzech Republic

South Korea

Finland

Austria

Singapore

United States

SloveniaFrance

Slovakia

Hungary

Ireland

Italy

Israel

Denmark

Netherlands

Poland

China

Mexico

Norway

Spain

Croatia

Belarus

Estonia

Thailand

Lithuania

Ukraine

Romania

Canada

MalaysiaLatvia

Hong Kong

Bulgaria

Russia

Portugal

Turkey

Bosnia and Herzegovina

Saudi Arabia

New Zealand

Panama

South Africa

United Arab Emirates

Lebanon

GreecePhilippines

India

Jordan

Uruguay

Argentina

Colombia

Costa Rica

Brazil

Tunisia

Moldova

Kuwait

El SalvadorMacedonia

Georgia

Mauritius

ZambiaChile

Egypt

Kyrgyzstan

Trinidad and Tobago

Australia

IndonesiaVietnam

Jamaica

Albania

Guatemala

Uganda

Liberia

Paraguay

Dominican Republic

KazakhstanOman

Qatar

Sri Lanka

Peru

Kenya

HondurasMoroccoPakistan

Uzbekistan

Zimbabwe

Iran

Bolivia

EcuadorNicaragua

MalawiTanzania

Cote dIvoire

Venezuela

GhanaRepublic of the Congo

Azerbaijan

Senegal

MaliCambodia

Madagascar

YemenEthiopia

MongoliaTajikistan

Bangladesh

Laos

SudanAlgeria

Mozambique

Gabon

Angola

Cameroon

NigeriaPapua New Guinea

MauritaniaLibya

Turkmenistan

Guinea

B

y=x

Figure 4: GCI and ECI comparisons for 2014

6Pearson correlation coefficient = 0.766, p-value= 0.5× 10−25.7Spearman’s rank correlation coefficient = 0.79, p-value= 5.2× 10−27.8The correlation between the ECI and GCI has remained relatively stable over the 1995-2014

period. These results are available upon request.

19

In Figure 5, we show the relationship between the GCI and log GDP/capita for

2014. Again, the positive relationship is not surprising9, but the variance in the

relationship provides additional insights into the current orientation of countries’

economies. Consistent with Figure 4, a number of resource-rich countries have

low GCI scores given their income. Germany, Italy, China, and India stand out

as having much higher GCI scores given their income per capita, suggesting that

their production capabilities are more oriented to the green economy than other

countries with a similar standard of living.

6 7 8 9 10 11 12

Log GDP/Capita

1

0

1

2

3

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Japan

Switzerland

Germany

SwedenUnited Kingdom

Czech Republic

South Korea

Finland

Austria

Singapore

United States

Slovenia

France

Slovakia

Hungary

Ireland

Italy

Israel

Denmark

Netherlands

Poland

China

Mexico

Norway

Spain

Croatia

Belarus

Estonia

Thailand

Lithuania

Ukraine

Romania

Canada

MalaysiaLatvia

Hong Kong

Bulgaria

Russia

Portugal

Turkey

Bosnia and Herzegovina

Saudi Arabia

New Zealand

Panama

South Africa

United Arab Emirates

Lebanon

GreecePhilippines

India

Jordan

Uruguay

ArgentinaColombia

Costa Rica

Brazil

Tunisia

Moldova

Kuwait

El SalvadorMacedoniaGeorgia

MauritiusZambia Chile

Egypt

Kyrgyzstan

Trinidad and TobagoAustralia

IndonesiaVietnam

JamaicaAlbania

GuatemalaUganda

LiberiaParaguay

Dominican Republic

KazakhstanOmanQatar

Sri Lanka

Peru

Kenya

HondurasMoroccoPakistanUzbekistanZimbabwe Iran

BoliviaEcuadorNicaragua

Malawi Tanzania

Cote dIvoireVenezuelaGhana Republic of the Congo

Azerbaijan

Senegal

Mali CambodiaMadagascar YemenEthiopia

MongoliaTajikistanBangladeshLaos

Sudan AlgeriaMozambique

GabonAngola

Cameroon

NigeriaPapua New GuineaMauritania LibyaTurkmenistanGuinea

Figure 5: GCI vs log GDP per capita for 2014

Finally, to provide some validation that the GCI is a useful estimate of green

production capabilities, we also examine the extent to which it can explain vari-

ation in environmentally relevant country characteristics. Specifically, we look at

the relationship between the GCI and countries’ environmental patenting rates,

9Pearson’s correlation coefficient = 0.55, p-value = 7.52× 10−11.

20

CO2/capita emissions, and the OECD’s measure of environmental policy strin-

gency (EPS).

Since the GCI and ECI can fluctuate year on year due to variability in trade data,

we use simple regressions on time-averaged explanatory variables as follows:

yi = xiβ + εi

where yi ∈ {Log Env. Patents, CO2/capita, Log EPS}, yi = 1N

∑t=tNt=t0

yit, xi =1N

∑t=TNt=t0

xit are time-averaged explanatory variables for N available periods, and

εi is the error term.

Table 4 shows the GCI’s ability to explain variation in environmentally relevant

variables over the twenty year period covered by our data. We find that the GCI

is strongly positively correlated with the number of environmental patents across

countries, even after controlling for their GDP/capita and ECI. We also find that

countries with higher GCI tend to have lower CO2 emissions. This relationship

is particularly interesting, given our dataset does not account for the emissions

intensity of each product’s production process. Additionally, we find a positive

relationship between the GCI and the OECD’s Environmental Policy Stringency

Index, suggesting there is some association between the environmental policies in

place in a country and its green production capabilities. While the results in Table

4 reflect GCI’s explanatory power over the long run, we also run regressions for

different years in Appendix A.7 and find consistent results.10

10We have also compared the GCI calculated using the Hausmann et al. (2014) PCI and Tac-chella et al. (2012)’s Product Complexity measure. As shown in Appendix A.8, both formulationsgive very similar results, suggesting that the GCI is robust to the choice of product complexitymeasure. It is also important to note that for this particular set of traded products, the com-plexity scores are fairly homogenous (see Figure 2), particularly when normalized to take a valuebetween 0 and 1. As such, the GCI score is very strongly correlated to a country’s green diversity(the number of green products it is competitive in). However, this will not necessarily be thecase for different product subsets, where there is greater variation in product complexity.

21

Table 4: Green Complexity Index

Log Env. Patents Log CO2/cap Log EPS

GCI 1.009*** −0.307*** 0.100***

(0.215) (0.102) (0.029)

ECI 1.158*** 0.290* −0.115**

(0.286) (0.157) (0.053)

Log GDP/Cap 0.116 0.850*** 0.213***

(0.128) (0.086) (0.034)

Intercept 1.593 −6.196*** −1.093***

(1.125) (0.734) (0.315)

Observations 1220 2318 558

Adjusted R2 0.766 0.765 0.7532

Robust standard errors in parenthesis.

Significance levels: * p < 0.1, ** p < 0.05, *** p < 0.01

Environmental patents data covers 2000 and 2005-2013, available from http://stats.oecd.

org/. CO2 (metric tons per capita) data covers 1995-2013, available from https://data.

worldbank.org/indicator/EN.ATM.CO2E.PC. Environmental Policy Stringency (EPS) data

covers 1995-2012, available from http://stats.oecd.org/. For all regressions, we take

country averages over all available time periods.

4.4 Green Adjacent Possible

While the GCI gives us an idea of which countries are currently competitive in

green products and technologies, successfully transitioning to the green economy

will no doubt require many countries to reorientate their existing productive struc-

ture and cultivate new green industries. Naturally, it would be helpful if countries

could identify green diversification opportunities that were relatively proximate to

their existing production capabilities, as this would allow them to take advantage

of skills, infrastructure and know-how that they already possess. To this end, we

introduce the Green Adjacent Possible (GAP), which aims to identify the most

proximate green diversification opportunities for each country.

In Figure 6, we illustrate the GAP for four countries with contrasting production

22

structures. In each panel, dots represent green products that countries do not

currently export competitively. The x-axis plots the density value for each green

product, which estimates how related that product is to the country’s current

capabilities. The y-axis measures each product’s PCI. We also label some of the

most proximate diversification opportunities for each country.

A number of things are interesting to note. As we would expect, Saudi Arabia is

much less proximate to the set of green products because its productive know-how

is more closely focused on extracting fossil fuel resources. Uganda is less proximate

to green products with higher PCI because it is a developing country with less ad-

vanced technological capabilities. However, Uganda could potentially build on its

agricultural base to diversify into green products made from vegetable materials,

such as screens and matting materials, which are used to prevent soil erosion. In

contrast, Germany’s advanced manufacturing base and significant existing exper-

tise in green products is reflected in its greater positive slope and high proximity

to high-PCI green products, such as optical devices (used in concentrated solar).

South Korea also has a slight positive slope, suggesting that its productive capa-

bilities are more oriented towards higher PCI green products such as transmission

shafts and static converters. In Appendix A.6, we construct the Green Product

Space (a network where green product nodes are linked to each other on the basis

of their relatedness) to provide further visualisations of the same four countries’

green production capabilities.

23

Optical devices

Glass fibres

Gas turbine engines(power < 5000kW)

Compressors for refrigerating equipment Static converters

Machines for balancing mechanical parts

Bicycle parts

Transmission shafts

Brooms and brushes madefrom vegetable material

Mats, screens, vegetable plaiting material

Polypropylene sheeting

Figure 6: Illustrating the Green Adjacent Possible for different countries.In each plot, the circles represent green products that the denoted country is notcompetitive in. The y-axis plots the PCI of each product and the x-axis plots thatproduct’s density to a country.

24

4.5 Green Complexity Potential

Recall that the Green Complexity Potential (GCP) measures each country’s av-

erage relatedness to green complex products it is not currently competitive in.

Hence, the GCP summarizes each country’s GAP into a single number and allows

us to compare countries in terms of their overall potential to diversify into green,

technologically sophisticated products.

In Table 5, we explore how predictive a country’s GCP is for future increases in

its green capabilities (as measured by the GCI), the number of green products it

is able to export competitively, and the share of green exports in its total export

basket. Specifically, we regress the countries’ GCP at the beginning of the period

(averaged over 1995—2000) on the change in countries’ GCI, number of compet-

itively exported green products and green export trade ratio at the end of the

period (averaged over 2009—2014) i.e.

∆yi = xiβ + εi

where yi ∈ {GCI, #Green exported products, Green exports}, ∆yi = 15

∑t=2014t=2009 yit−

15

∑t=2000t=1995 yit, xi = 1

5

∑t=2000t=1995 xit are explanatory variables averaged at the begin-

ning of the sample, and εi is the error term. This specification is similar to the

approach taken by O’Clery et al. (2016). However, to ensure our results are robust

to year-on-year trade data fluctuations, we take 5-year averages.

Controlling for countries’ current incomes and ECI, we find that countries with

higher GCP scores are more likely to have greater future increases in their GCI,

green export trade ratio, and number of green products they are able to export

competitively. In Appendix A.7, we show that the predictive power of the GCP is

robust to different time-averaging specifications.

25

Table 5: Green Complexity Potential

∆ GCI ∆ #Green exported ∆ Green export

(t+ δ) products (t+ δ) trade ratio (t+ δ)

Log GCP(t) 0.172*** 7.118*** 0.012***

(0.038) (1.678) (0.003)

Log GDP/Cap(t) −0.005 −0.448 0.001

(0.024) (1.135) (0.002)

ECI(t) −0.143** −7.450*** −0.006*

(0.043) (2.112) (0.004)

GCI(t) −0.060

(0.051)

Green exported products(t) 0.084

(0.057)

Green export trade ratio(t) −0.075

(0.158)

Intercept 0.577** 29.715*** 0.039**

(0.245) (11.110) (0.016)

Observations 1220 1220 1220

Adjusted R2 0.203 0.212 0.169

Robust standard errors in parenthesis.

Significance levels: * p < 0.1, ** p < 0.05, *** p < 0.01

t relates to country averaged values over years 1995-2000 and t+ δ relates to country averaged values over years

2009-2014. #Green exported products refers to the number of green exports in which the country has RCA > 1

(i.e. diversity).

Figure 7 shows the relationship between the GCP and GCI for 2014. Panel A

shows the relationship between the GCP and GCI values, while Panel B shows

the relationship between the GCP and GCI ranks. In both cases, we find a strong

correlation which indicates that the more green production capabilities a country

has, the easier it is to diversify into additional new green products.11

11Panel A: Pearson correlation coefficient = 0.921, p-value= 3.49× 10−51, Panel B: Spearmancorrelation coefficient = 0.951, p-value= 2.11× 10−63.

26

1 0 1 2 3

Green Complexity Index

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Japan

Switzerland

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Sweden

United KingdomCzech Republic

South KoreaFinland

Austria

Singapore

United States

Slovenia

France

SlovakiaHungary

Ireland

Italy

Israel

Denmark

Netherlands

Poland

China

Mexico

Norway

Spain

Croatia

Belarus

EstoniaThailand

Lithuania

Ukraine

Romania

CanadaMalaysia

LatviaHong Kong

Bulgaria

Russia

PortugalTurkey

Bosnia and Herzegovina

Saudi Arabia

New ZealandPanama

South Africa

United Arab Emirates

LebanonGreece

Philippines

India

Jordan

Uruguay

Argentina

ColombiaCosta Rica

Brazil

Tunisia

Moldova

Kuwait

El SalvadorMacedonia

Georgia

Mauritius

Zambia

Chile

Egypt

Kyrgyzstan

Trinidad and Tobago

Australia

IndonesiaVietnam

Jamaica

Albania

Guatemala

Uganda

Liberia

Paraguay

Dominican Republic

KazakhstanOman

Qatar

Sri Lanka

PeruKenyaHonduras

Morocco

Pakistan

UzbekistanZimbabweIran

BoliviaEcuadorNicaraguaMalawi

Tanzania

Cote dIvoire

Venezuela

GhanaRepublic of the CongoAzerbaijan

Senegal

Mali

CambodiaMadagascar

YemenEthiopia

MongoliaTajikistan

Bangladesh

LaosSudanAlgeria

Mozambique

GabonAngola

CameroonNigeriaPapua New GuineaMauritaniaLibyaTurkmenistanGuinea

A

120 100 80 60 40 20 1

Green Complexity Index Rank

120

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South KoreaFinland

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United States

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France

SlovakiaHungary

Ireland

Italy

Israel

DenmarkNetherlands

Poland

China

Mexico

Norway

Spain

Croatia

Belarus

EstoniaThailand

Lithuania

Ukraine

Romania

CanadaMalaysia

LatviaHong Kong

Bulgaria

Russia

PortugalTurkey

Bosnia and Herzegovina

Saudi Arabia

New Zealand

Panama

South Africa

United Arab Emirates

LebanonGreece

Philippines

India

Jordan

Uruguay

Argentina

ColombiaCosta Rica

Brazil

Tunisia

Moldova

Kuwait

El SalvadorMacedonia

Georgia

Mauritius

Zambia

Chile

Egypt

Kyrgyzstan

Trinidad and Tobago

Australia

IndonesiaVietnam

Jamaica

Albania

Guatemala

Uganda

Liberia

Paraguay

Dominican Republic

Kazakhstan

Oman

Qatar

Sri Lanka

Peru KenyaHonduras

Morocco

Pakistan

UzbekistanZimbabwe

Iran

Bolivia

Ecuador

Nicaragua Malawi

Tanzania

Cote dIvoire

Venezuela

Ghana

Republic of the CongoAzerbaijan

Senegal

Mali

CambodiaMadagascar

Yemen

Ethiopia

MongoliaTajikistan

Bangladesh

Laos

Sudan

Algeria

Mozambique

Gabon

Angola

Cameroon

Nigeria

Papua New GuineaMauritania

LibyaTurkmenistan

Guinea

B

y=x

Figure 7: GCP and GCI comparison for 2014

However, the differences between the GCI and GCP provide additional informa-

tion about future growth: countries including China, Spain, Turkey, India and

the Netherlands have significantly higher GCP than GCI, suggesting that these

countries may be particularly well-positioned for fast development of future green

capabilities. In contrast, while countries such as the US, Japan, and Denmark

currently have very strong green production capabilities, their lower GCP scores

indicate that future expansion into new green product markets could be relatively

slower.

4.6 Green stimulus packages and green production capa-

bilities

Finally we turn to the question of whether direct government intervention can in-

fluence green production capabilities. Here, we present some preliminary evidence

to suggest that policy can make a difference. We analyse data on green stimulus

packages in 19 countries over the early years of the global financial crisis (Barbier

27

et al., 2010). Many countries embarked on stimulus programmes to boost their

weak economies and green spending formed a significant part of the stimulus. As

shown in Table 6, even after controlling for GDP per capita, the size of the stimu-

lus packages is positively associated with increases in (i) the GCI, (ii) the number

of green exports that the country is competitive in, and (iii) the ratio of green

exports to total exports between 2008 and 2011 (this holds both for stimulus and

stimulus per capita, see Appendix A.7).

Table 6: Green Stimulus Analysis

∆ GCI ∆ #Green exported ∆ Green export

(t+ δ) products (t+ δ) trade ratio (t+ δ)

Green Stimulus 0.970*** 41.565*** 0.027*

(0.205) (9.699) (0.015)

Log GDP/Cap(t) 0.000** 0.000** 0.0000

(0.000) (0.000) (0.0000)

GCI(t) 0.054*

(0.027)

#Green exported products(t) 0.076**

(0.029)

Green export trade ratio(t) 0.082*

(0.043)

Intercept 0.056 −0.337 −0.007*

(0.047) (2.204) (0.003)

Observations 19 19 19

Adjusted R2 0.495 0.495 0.265

Robust standard errors in parenthesis.

Significance levels: * p < 0.1, ** p < 0.05, *** p < 0.01

t relates to the year 2008 and (t+ δ) relates to the year 2011.

Green Stimulus units are US ’000 per capita. Green Stimulus data are from Table 1 of Barbier et al. (2010), and

relates to low carbon support for renewable energy, carbon capture and sequestration, energy efficiency, public

transport and rail, and improving electrical grid transmission. #Green exported products refers to the number of

green exports in which the country has RCA > 1 (i.e. diversity).

28

5 Discussion and conclusion

This paper has advanced a novel, data-driven approach to analyse green production

capabilities across countries. Our results have a number of policy implications.

First, we are able to identify and measure trade in a much more extensive set of

green products by drawing on a number of international agreements and indepen-

dent policy sources. Our dataset is therefore a robust, consensus-driven definition

of green products that can be used in research and policy. Our results in section

4.1 highlight that green and renewable products have not grown as a fraction of

total trade in the last twenty years. Given the urgency of the transition towards

a green economy, agreements to advantage trade in these products might play

an important role. The reason is that, as we show in this paper, many green

and renewable energy products have high PCI values and could consequently be

difficult—at least in the short run—for less technologically advanced countries to

export competitively.

Second, we estimate which countries are currently best positioned to thrive in the

green economy thereby shedding light on an increasingly important question for

policymakers. We also show how our estimates of countries’ green capabilities

have evolved over time. A country that finds itself sliding down the GCI or GCP

ranking may want to strengthen policies aimed at increasing its green capabilities.

Our work not only complements many recent papers on this topic (Huberty and

Zachmann, 2011; Hamwey et al., 2013; Fankhauser et al., 2013; Fraccascia et al.,

2018), but also provides a more extensive coverage of countries and green products.

Moreover, our novel GCI and GCP measures demonstrate how analytical meth-

ods from economic complexity can be gainfully employed to understand possible

pathways towards a green economy.

Third, our results show that green diversification is path-dependent. The strong

positive correlation between the GCI and GCP suggests that early success in gain-

ing green capabilities better enables countries to develop more green capabilities

in the future. Additionally, countries with production capabilities too narrowly

focused on resource extraction activities may find that their green production ca-

29

pabilities are underdeveloped and their competitive advantage is less aligned with

the direction of the future green economy.

The path dependence in green capability accumulation tentatively indicates a role

for industrial policy (see, for example, Aghion et al., 2011; Hallegatte et al., 2013;

Rodrik, 2014). Our results do not pin down a specific and advantageous green

industrial policy. However, by identifying the GAP, we can provide concrete in-

dications of where the next competitive green opportunities for each country are

likely to be. But whether a transition to these technologies requires interventionist

industrial policy or regulatory reform needs to be decided on a case-by-case basis.

Our green stimulus results only provide some indication that government policy

can have an effect on green capabilities. It is also important to stress that the

GCI, GCP, and GAP only reflect the particular orientation of countries’ current

export baskets and do not account for domestic or service-based green production

capabilities that may exist within these countries. Green industrial policy should

ideally take all green capabilities and all relevant domestic policy objectives and

constraints into account.

There are plenty of fruitful areas for further work. First, we have only considered

capabilities based on export data. While the GCI and GCP explain variation in

environmentally relevant measures across countries, we do not account for capa-

bilities embodied only in services (Stojkoski et al., 2016; OECD, 2017) or in goods

sold only domestically. Second, we do not account for occupation-specific skills

relevant for new green economy products (Neffke and Henning, 2013). Third, we

do not look at regional or city-level variation (Boschma et al., 2013; O’Clery et al.,

2016). Fourth, we have not considered channels for green technology diffusion

across neighbouring countries (Bahar et al., 2014). Fifth, we have not explored

regional green industrial policy or regional specialisation (e.g. Pearl River Delta,

Silicon Valley); government policy and research might want to focus directly on

the competitiveness of regional production clusters (Delgado et al., 2014). Fi-

nally, it would be worth understanding what roles services, skills, and regional

specialisation play in a more expanded definition of the green economy by looking

closely at green patents, green research and development, carbon emissions, and

environmental protection.

30

A Appendix

A.1 Description of the data sources

Table 7: Green Product Data Sources

List Description Source

WTO Reference Universe408 products that represent a universe of po-tentially green products proposed by differ-ent WTO Member States

• WTO Report by the Chairman to the Trade Negoti-ations Committee on the Committee and Trade andEnvironment in Special Session TN/TE/19 (22 March2010)

• WTO Report by the Chairman to the Trade Negoti-ations Committee on the Committee and Trade andEnvironment in Special Session TN/TE/20 (21 April2011)

WTO Sample Core List26 products with wide endorsement fromWTO Member States

• WTO Report by the Chairman to the Trade Negoti-ations Committee on the Committee and Trade andEnvironment in Special Session TN/TE/20 (21 April2011)

APEC List of Environmen-tal Goods

54 green products for which APEC Memberstates agreed to reduce applied tariff rates to5% or less by the end of 2015

• 2012 APEC Leaders Declaration Annex C

OECD (1999) IllustrativeProduct List of Environ-mental Goods

List of 121 illustrative environmental prod-ucts developed by the OECD/Eurostat In-formal Working Group

• OECD (1999), “Future Liberalisation of Trade in En-vironmental Goods and Services: Ensuring Environ-mental Protection as well as Economic Benefits”

• A Comparison of the APEC and OECD Lists”, OECDTrade and Environment Working Paper No. 2005-04Table A1.

List of 257 customizedproducts developed by theOECD

List of 257 customized products developedby the OECD

• Sauvage (2014), “The Stringency of EnvironmentalRegulations and Trade in Environmental Goods”,OECD Trade and Environment Working Papers,2014/03

A.2 Data advantages and limitations

The green product classifications we use to construct our two product lists of-fer a number of advantages. First, for each proposed product in the WTO andOECD lists, it is possible to identify one (or more) environmental category thatthe product falls under. Although the WTO and OECD differ in the structureof their environmental categories, they are still broadly consistent and helpful foridentifying a product’s environmental purpose (such as renewable energy, wastewater management, energy efficiency etc.) Second, the APEC and WTO lists alsoinclude specific information about each product’s environmental benefits. Thisinformation was provided by member countries of the respective organisations asrationale for a proposed product’s environmental endorsement. Thirdly, the APECand WTO lists also indicate the set of member countries endorsing a given prod-uct as green. This information is useful for helping gauge the level of consensusassociated with each product’s environmental status.

31

A number of limitations are also important to keep in mind. First, the HS system(which classifies products for the purpose of trade and tariffs) was not set upto account for the environmental benefits of products. This can sometime resultin poor alignment between a recognized environmental product (such as a windturbine) and its most relevant HS code.12 Second, many products are dual use,which means they can have both environmental and non-environmental purposes.Although WTO and APEC classifications provide “ex-outs” (a further descriptionto identify relevant environmental products classified under the HS code), it canbe very challenging to identify the precise environmental trade flow associatedwith a particular ex-out for a given HS category. As such, our analysis (whichis based trade volumes for entire HS-6 commodity codes) will tend to somewhatover-estimate environmental trade volumes. Finally, our dataset does not provideinformation about the production process of a given product, only its use-orientedbenefits. Consequently, our data do not allow us to examine the environmentalimpact of product production and use (e.g. lifecycle emissions of a product).

A.3 Top exporters of green and renewable products (bytrade volume)

Figure 8 shows the top exporters of all green products (by trade volume). PanelA presents leaders in absolute terms. While the US was the largest green exporterfrom 1995-2003, Germany took over in 2004 but was in turn displaced by China in2010. Panel B shows the green exports of these same countries, but instead as aproportion of each country’s total exports. Denmark has had the highest relativeshare of green exports – peaking over the financial crisis period at around 14 percent. Of all these countries, South Korea has seen the largest “greening” of itsexport basket – its green exports increased as a percentage of total exports fromaround 6 percent in 2002 to around 12 percent in 2010.

12For example, a key relevant HS code for identifying wind turbine towers is a very broad HScategory - 730820 - which relates to “Towers and lattice masts, iron or steel”.

32

1995 2000 2005 2010 20140

50

100

150

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250

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Tota

l G

reen E

xport

s ($

US B

illio

n)

A

China

Germany

United States

Japan

Italy

South Korea

France

Mexico

United Kingdom

Netherlands

Spain

Switzerland

Canada

Austria

Poland

Czech Republic

Malaysia

Singapore

Sweden

Thailand

Denmark

1995 2000 2005 2010 20140.00

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0.10

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0.25

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xport

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roport

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Germany

United States

Japan

Italy

South Korea

France

Mexico

United Kingdom

Netherlands

Spain

Switzerland

Canada

Austria

Poland

Czech Republic

Malaysia

Singapore

Sweden

Thailand

Denmark

Figure 8: Top 20 Exporters of Green Products

Figure 9 shows the top exporters of renewable energy products. Again, Panel Apresents the leading countries in absolute terms. As before, China has becomethe largest exporter of renewable energy products and its export dominance inrenewable energy products (in some years exceeding $20 billion) is even greaterthan its dominance in all green products. In Panel B, we show the same countries’renewable energy exports relative to each nation’s total exports. Here, SouthKorea’s and Denmark’s rapid patterns of green export growth become even moreprominent.

33

1995 2000 2005 2010 20140

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40

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Tota

l R

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able

Energ

y E

xport

s ($

US B

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Germany

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South Korea

Japan

Italy

France

Mexico

United Kingdom

Netherlands

Spain

Switzerland

Denmark

Malaysia

Poland

Hungary

Czech Republic

Canada

Sweden

Thailand

Singapore

1995 2000 2005 2010 20140.00

0.02

0.04

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Tota

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enew

able

Energ

y E

xport

s as

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l Export

s

B

China

Germany

United States

South Korea

Japan

Italy

France

Mexico

United Kingdom

Netherlands

Spain

Switzerland

Denmark

Malaysia

Poland

Hungary

Czech Republic

Canada

Sweden

Thailand

Singapore

Figure 9: Top 20 Exporters of Renewable Energy Products

34

A.4 Products

Table 8: Top 10 Green Products by PCI

Rank PCI HS6 Code Product Description Environmental Benefits Environmental Lists

1 2.5073 901380Optical devices, appliances and in-struments, nes

Solar Heliostats (Heliostats orient mirrors in concentrated solar power systems to reflect sunlight on to aCSP receiver)

APEC, OECD (2014)

2 2.0716 902790Microtomes, parts of scientific anal-ysis equipment

Microtomes are devices that prepare slices of samples for analysis - used in environmental monitoring APEC, OECD (1999), OECD (2014)

3 2.0134 847989Machines and mechanical appli-ances, nes

Machines and appliances designed for a wide range of areas of environmental management including waste,waste water, drinking water production and soil remediation

WTO Sample, APEC, OECD (1999), OECD (2014)

4 1.8805 902730Spectrometers, spectrophotome-ters, etc using light

Used in a wide range of environmental applications, including identification of unknown chemicals, tox-ins and trace contaminants, environmental control, water management, food processing, agriculture andweather monitoring

WTO Sample, APEC, OECD (1999), OECD (2014)

5 1.8625 902780Equipment for physical or chemicalanalysis, nes

Used to measure, record, analyse and assess environmental samples or environmental influences APEC, OECD (1999), OECD (2014)

6 1.8291 680690Mineral heat or sound insulatingmaterials and articles

Used for heat and energy management OECD (2014)

7 1.8119 902720Chromatographs, electrophoresisinstruments

Used to monitor and analyse air pollution emissions, ambient air quality, water quality, etc. APEC, OECD (1999), OECD (2014)

8 1.8077 902710 Gas/smoke analysis apparatus Used for monitoring and analysing environmental pollution. APEC, OECD (1999), OECD (2014)

9 1.7945 847990Parts of machines and mechanicalappliances nes

Parts for environmental management devices (Machines and appliances designed for a wide range of areas ofenvironmental management including waste, waste water, drinking water production and soil remediation)

APEC, OECD (2014)

10 1.7795 848360Clutches, shaft couplings, universaljoints

Used for initial assembly, repair, and maintenance of wind energy systems OECD (2014)

Table 9: Bottom 10 Green Products by PCI

Rank PCI HS6 Code Product Description Environmental Benefits Environmental Lists

284 −1.2445 871200Bicycles, other cycles, not motor-ized

Cleaner or more resource efficient technology or product OECD (2014)

285 −1.2826 871411 Motorcycle Saddles Cleaner or more resource efficient technology or product OECD (2014)

286 −1.2935 220710Undenatured ethyl alcohol > 80%by volume

Renewable Energy Plant OECD (1999)

287 −1.4189 560790Twine, cordage, ropes and cables,of other materials

Environmentally preferrable product OECD (2014)

288 −1.5074 960310Brooms/brushes of vegetable mate-rial

Waste collection equipment (solid waste management) OECD (1999)

289 −1.6088 560721Binder or baler twine, of sisal oragave

Environmentally preferrable product OECD (2014)

290 −1.864 460120Mats, matting and screens, veg-etable plaiting material

Environmentally preferrable product WTO Sample

291 −2.1905 530599Vegetable fibre nes, processed notspun, tow and waste

Environmentally preferrable product OECD (2014)

292 −2.2365 630510Sacks and bags, packing, of jute orother bast fibres

Environmentally preferrable product OECD (2014)

293 −2.9908 530310Jute and other textile bast fibres,raw or processed but not spun

Environmentally preferrable product OECD (2014)

Table 10: Top 10 Renewable Energy Products by PCI

Rank PCI HS6 Code Product Description Environmental Benefits Environmental Lists

1 2.5073 901380Optical devices, appliances and in-struments, nes

Solar Heliostats (Heliostats orient mirrors in concentrated solar power systems to reflect sunlight on to aCSP receiver)

APEC, OECD (2014)

2 2.0134 847989Machines and mechanical appli-ances nes

Machines and appliances designed for a wide range of areas of environmental management including waste,waste water, drinking water production and soil remediation

WTO Sample, APEC, OECD (1999), OECD (2014)

3 1.7795 848360Clutches, shaft couplings, universaljoints

Used for initial assembly, repair, and maintenance of wind energy systems OECD (2014)

4 1.7554 848340Gearing, ball screws, speed chang-ers, torque converte

Gearboxes transform the rotation of the blades of wind turbines into the speed required to produce renew-able electricity

OECD (2014)

5 1.7419 841199Parts of gas turbine engines exceptturbo-jet/prop

Parts for gas turbines, which generate electrical power from recovered landfill gas, coal mine vent gas, orbiogas

APEC, OECD (2014)

6 1.4104 841181Gas turbine engines nes of a power< 5000 kW

Gas turbines for electrical power generation from recovered landfill gas, coal mine vent gas, or biogas (cleanenergy system)

WTO Sample, OECD (2014)

7 1.2239 840619 Steam and vapour turbines nesTurbines designed for the production of geothermal energy (renewable energy) and co-generation ((CHP)which allows for a more effective use of energy than conventional generation)

WTO Sample, OECD (2014)

8 1.216 903289Automatic regulating/controllingequipment nes

Used in renewable energy and smart grid applications, as well as other process control instruments andapparatus for temperature, pressure, flow and level, and humidity

APEC, OECD (1999), OECD (2014)

9 1.1399 841950Heat exchange units, non-domestic,non-electric

Provide cooling effect to heat exchangers in solar collector or solar system controllers to avoid overheating.Heat exchangers are also used in geothermal energy systems.

WTO Sample, OECD (1999), OECD (2014)

10 1.1216 840690 Parts of steam and vapour turbinesParts for turbines designed for production of geothermal energy (renewable energy) and co- generation((CHP) which allows for a more effective use of energy than conventional generation

APEC, OECD (2014)

Table 11: Bottom 10 Renewable Energy Products by PCI

Rank PCI HS6 Code Product Description Environmental Benefits Environmental Lists

48 −0.14782 761100Aluminium reservoirs,vats, tanks,etc, volume >300l

Containers for the production of biogas, waste water management, drinking water production and solarthermal energy purposes.

OECD (2014)

49 −0.31929 850432Transformers electric, power capac-ity 1-16 KVA, nes

Renewable Energy Plant OECD (2014)

50 −0.36528 850421Liquid dielectric transformers <650 KVA

Used for initial assembly, repair, and maintenance of wind energy systems OECD (2014)

51 −0.38168 850161AC generators, of an output < 75kVA

Used in conjunction with boiler and turbines to generate electricity in renewable energy plants OECD (2014)

52 −0.41086 850720Lead-acid electric accumulators ex-cept for vehicles

Provides for energy storage in off-grid PV system OECD (2014)

53 −0.56155 700992 Glass mirrors, framed Renewable Energy Plant OECD (2014)

54 −0.61976 730820Towers and lattice masts, iron orsteel

Used to elevate and support a wind turbine for the generation of renewable energy WTO Sample, OECD (2014)

55 −0.63559 290511 Methyl alcohol Renewable Energy Plant OECD (1999)

56 −0.81094 850431Transformers electric, power capac-ity < 1 KVA, nes

Renewable Energy Plant OECD (2014)

57 −1.2935 220710 Undenatured ethyl alcohol > 80

A.5 Countries

In Table 12, we present each country’s GCI, GCP and ECI ranks for 2014. Wealso identify each country’s most proximate green product that they are not yetcompetitive in and show the density of that product to the given country.

Table 12: Country Rankings and most proximate green product for 2014

Country GCI Rank GCP Rank ECI Rank Most proximate green product Proximity DensityGermany 1 4 3 Webs, mattresses, other nonwoven fibreglass products 0.523527Italy 2 1 24 Multiple-walled insulating units of glass 0.551191United States 3 8 5 Vacuum pumps 0.393836Austria 4 7 10 Manostats 0.407972

Denmark 5 18 20Mineral and aerated waters not sweetened orflavoured

0.338434

China 6 2 38 Jute and other textile bast fibres, raw or retted 0.547997Czech Republic 7 12 9 Parts of wash, filling, closing, aerating machinery 0.357498France 8 5 12 Valves, pressure reducing 0.4277Japan 9 15 1 Railway maintenance-of-way service vehicles 0.355749

United Kingdom 10 13 11Compression refrigeration equipment with heat ex-change

0.335603

Sweden 11 17 4 Gas/smoke analysis apparatus 0.316329

Spain 12 3 29Mineral and aerated waters not sweetened orflavoured

0.486129

Slovenia 13 19 13 Domestic iron/steel solid fuel appliances, not cooker 0.299162

Poland 14 9 23Mineral and aerated waters not sweetened orflavoured

0.398532

Hungary 15 23 16 Manostats 0.267572Finland 16 30 6 Mufflers and exhaust pipes for motor vehicles 0.228582Portugal 17 10 48 Brooms/brushes of vegetable material 0.422045

Estonia 18 24 28Mineral and aerated waters not sweetened orflavoured

0.284672

Switzerland 19 32 2 Clutches, shaft couplings, universal joints 0.230932Romania 20 22 39 Liquid dielectric transformers < 650 KVA 0.282028Croatia 21 26 36 Building blocks, bricks of cement, or artificial ston 0.307701Mexico 22 40 22 Gas supply/production/calibration meters 0.170645Slovakia 23 28 18 Prefabricated structural items of cement or concrete 0.241184

Bulgaria 24 20 46Mineral and aerated waters not sweetened orflavoured

0.327624

Turkey 25 6 56 Brooms/brushes of vegetable material 0.462755Lithuania 26 16 34 Cans, iron/steel, capacity <50l closed by crimp/solde 0.336749South Korea 27 29 8 Bicycle brakes, parts thereof 0.245542India 28 11 50 Brooms/brushes of vegetable material 0.388543Israel 29 34 21 Surveying, etc instruments nes 0.1938Latvia 30 25 35 Tank, cask or container, iron/steel, capacity 50-300l 0.280853Malaysia 31 41 27 Parts and accessories of optical appliances nes 0.182011Lebanon 32 31 58 Building blocks, bricks of cement, or artificial ston 0.262736

Thailand 33 21 40Mats, matting and screens, vegetable plaiting mate-rial

0.268803

Netherlands 34 14 15 Surveying, etc instruments nes 0.344208Singapore 35 47 7 Optical devices, appliances and instruments, nes 0.183491Ukraine 36 42 31 Sheet etc, cellular of polymers of styrene 0.189066Bosnia andHerzegovina

37 39 47 Liquid dielectric transformers < 650 KVA 0.216554

Tunisia 38 43 76 Sacks and bags, packing, of jute or other bast fibres 0.231006

Belarus 39 46 30Mineral and aerated waters not sweetened orflavoured

0.186029

Norway 40 66 17 Anhydrous ammonia 0.112998South Africa 41 44 43 Sacks and bags, packing, of jute or other bast fibres 0.205328Canada 42 37 19 Railway cars nes, closed and covered 0.204049Philippines 43 49 64 Brooms/brushes of vegetable material 0.167938Greece 44 33 52 Sacks and bags, packing, of jute or other bast fibres 0.24185Hong Kong 45 27 41 Bicycle hubs, free-wheel sprocket wheels 0.264135Brazil 46 52 32 Railway cars nes, closed and covered 0.139184Vietnam 47 38 92 Jute and other textile bast fibres, raw or retted 0.282421Egypt 48 35 71 Sacks and bags, packing, of jute or other bast fibres 0.278484Indonesia 49 36 78 Jute and other textile bast fibres, raw or retted 0.273387

Moldova 50 57 77Mineral and aerated waters not sweetened orflavoured

0.159036

Jordan 51 55 62 Sacks and bags, packing, of jute or other bast fibres 0.181749Ireland 52 62 14 Surveying, etc instruments nes 0.125117Kenya 53 63 82 Sacks and bags, packing, of jute or other bast fibres 0.195668Russia 54 70 25 Gas turbine engines nes of a power < 5000 kW 0.10655Uganda 55 64 67 Undenatured ethyl alcohol > 80% by volume 0.166241Senegal 56 69 72 Surveying, etc instruments nes 0.134271El Salvador 57 54 83 Brooms/brushes of vegetable material 0.183812

Continued on next page

37

Table 12 – continued from previous pageCountry GCI Rank GCP Rank ECI Rank Most proximate green product Proximity Density

New Zealand 58 50 33 Surveying, etc instruments nes 0.164334Macedonia 59 58 73 Brooms/brushes of vegetable material 0.169198Dominican Re-public

60 59 74 Sacks and bags, packing, of jute or other bast fibres 0.179649

United Arab Emi-rates

61 81 57 Anhydrous ammonia 0.098455

Sri Lanka 62 51 110 Sacks and bags, packing, of jute or other bast fibres 0.24724Malawi 63 87 70 Surveying, etc instruments nes 0.072619Guatemala 64 48 85 Sacks and bags, packing, of jute or other bast fibres 0.237824Costa Rica 65 77 55 Chlorine 0.089105Tanzania 66 72 96 Sacks and bags, packing, of jute or other bast fibres 0.169185Argentina 67 61 37 Methyl alcohol 0.133796Georgia 68 78 60 Undenatured ethyl alcohol > 80% by volume 0.088708Pakistan 69 45 107 Jute and other textile bast fibres, raw or retted 0.307798Morocco 70 53 100 Brooms/brushes of vegetable material 0.2086Honduras 71 67 91 Undenatured ethyl alcohol > 80% by volume 0.14617Cameroon 72 94 80 Undenatured ethyl alcohol > 80% by volume 0.054028Albania 73 68 109 Brooms/brushes of vegetable material 0.162402Colombia 74 73 54 Undenatured ethyl alcohol > 80% by volume 0.101699Kyrgyzstan 75 83 95 Sacks and bags, packing, of jute or other bast fibres 0.083056Peru 76 65 84 Sacks and bags, packing, of jute or other bast fibres 0.149909

Oman 77 100 61Mineral and aerated waters not sweetened orflavoured

0.044163

Mauritius 78 56 89 Brooms/brushes of vegetable material 0.186092Zimbabwe 79 82 75 Jute and other textile bast fibres, raw or retted 0.088834Australia 80 71 44 Methyl alcohol 0.112851Madagascar 81 74 117 Brooms/brushes of vegetable material 0.148984Kazakhstan 82 90 45 Anhydrous ammonia 0.07086Kuwait 83 106 42 Methyl alcohol 0.032296Uruguay 84 75 49 Undenatured ethyl alcohol > 80% by volume 0.0896Uzbekistan 85 85 99 Sacks and bags, packing, of jute or other bast fibres 0.08202Mozambique 86 96 104 Jute and other textile bast fibres, raw or retted 0.072479Ethiopia 87 88 114 Sacks and bags, packing, of jute or other bast fibres 0.10837Iran 88 95 59 Liquid dielectric transformers < 650 KVA 0.042025Bangladesh 89 80 122 Brooms/brushes of vegetable material 0.14754Nicaragua 90 86 115 Sacks and bags, packing, of jute or other bast fibres 0.111789Panama 91 60 68 Sacks and bags, packing, of jute or other bast fibres 0.169703Yemen 92 99 79 Sacks and bags, packing, of jute or other bast fibres 0.056184Cote dIvoire 93 91 94 Surveying, etc instruments nes 0.066838Paraguay 94 89 86 Sacks and bags, packing, of jute or other bast fibres 0.069216Ecuador 95 93 98 Sacks and bags, packing, of jute or other bast fibres 0.067348

Jamaica 96 84 66Mineral and aerated waters not sweetened orflavoured

0.082543

Ghana 97 97 102 Undenatured ethyl alcohol > 80% by volume 0.059005Chile 98 76 53 Anhydrous ammonia 0.091139Laos 99 98 119 Sacks and bags, packing, of jute or other bast fibres 0.070638Mali 100 102 93 Sacks and bags, packing, of jute or other bast fibres 0.056515Republic of theCongo

101 111 81 Sacks and bags, packing, of jute or other bast fibres 0.023712

Saudi Arabia 102 103 26 Manganese oxides other than manganese dioxide 0.033556Zambia 103 92 65 Sacks and bags, packing, of jute or other bast fibres 0.066985Cambodia 104 79 121 Jute and other textile bast fibres, raw or retted 0.153735Gabon 105 115 87 Surveying, etc instruments nes 0.017623Venezuela 106 118 69 Surveying, etc instruments nes 0.011949Guinea 107 110 113 Sacks and bags, packing, of jute or other bast fibres 0.03639Qatar 108 120 51 Buoys, beacons, coffer-dams, pontoons, floats nes 0.009239Trinidad and To-bago

109 107 63Mineral and aerated waters not sweetened orflavoured

0.029218

Algeria 110 119 88 Sodium hydroxide (caustic soda) solid 0.009689Nigeria 111 108 108 Jute and other textile bast fibres, raw or retted 0.029281Bolivia 112 104 103 Jute and other textile bast fibres, raw or retted 0.042712Papua NewGuinea

113 114 118 Sacks and bags, packing, of jute or other bast fibres 0.02736

Mongolia 114 105 97 Sacks and bags, packing, of jute or other bast fibres 0.032406Sudan 115 112 120 Sacks and bags, packing, of jute or other bast fibres 0.033396Libya 116 121 106 Sacks and bags, packing, of jute or other bast fibres 0.010857Liberia 117 117 111 Surveying, etc instruments nes 0.016883Tajikistan 118 101 105 Jute and other textile bast fibres, raw or retted 0.046313Azerbaijan 119 113 90 Methyl alcohol 0.020978Angola 120 122 112 Methyl alcohol 0.003356Mauritania 121 109 101 Sacks and bags, packing, of jute or other bast fibres 0.027647Turkmenistan 122 116 116 Sacks and bags, packing, of jute or other bast fibres 0.018702

38

A.6 Green Product Space

To visualize the relatedness in capabilities underpinning green products, we followHidalgo et al. (2007) and construct a hierarchically clustered network where greenproducts are linked to other green products if they have a high probability of beingco-exported. To create this network, we construct a maximum spanning tree13

from the weighted matrix φ and add additional edges with proximity greater thana given threshold (here we use a proximity threshold = 0.37).14 This ensures weonly connect green products that have a high probability of being co-exported.We show the resulting network, the Green Product Space, in Figure 10.

Similar to Hidalgo et al.’s (2007) product space for the entire set of traded products,we find green products with lower PCI tend to be located in the periphery of thegreen product space, while products with higher PCI are located in the core.This is interesting from a green diversification-oriented development perspective:while it may be relatively easy to export green products with lower PCI, theaccumulated capabilities may have limited spillover opportunities into other greenproducts. However, as green products with higher PCI tend to be related to manyother green products, gaining capabilities to export high-PCI green products couldprovide greater future green industrial development possibilities.

The Green Product Space also provides a new way to visualize each country’scompetitive green exports. We show a selection of different countries in Figure 11.Holding the underlying network fixed, we colour (in green) products that a givencountry exports competitively. While the most striking aspect of Germany’s ex-port basket is the sheer abundance of competitive green products, it is interestingto note that the majority of these are located in the core of the Green ProductSpace. South Korea also competitively exports a number of complex green prod-ucts located in the Green Product Space core, but specializes in a distinct branchof green products relating to solar photovoltaics and batteries. As a developingcountry, Uganda currently exports fewer green products - many of which are lesscomplex and tending relate to vegetable materials. Finally and unsurprisingly,Saudi Arabia currently exports very few green products - all located around theperiphery of the Green Product Space.

13A spanning tree of a given graph is a tree (contains no cycles) that connects all verticeswith the minimum possible number of edges. A maximum spanning tree is a spanning tree of aweighted graph that has the maximum weight. That is, it connects nodes by adding edges withthe largest weight until the graph is fully connected.

14Alternative thresholds give similar results.

39

Bicycle'frames'

Bicycle'saddles'

Bicycle'hubs'Bicycle'brakes'

Bicycle'pedals'

Bicycle'wheels' Filament'lamps'

Fluorescent'lamps'

Parts'of'electrical'transformers'&'conductors'

Electric'fans'

Sta<c'converters'

Ballasts'for'discharge'lamps'

Fish?hooks'

Fishing'nets'of'manmade'tex<le'material'

Self?propelled'tamping'machines'Air?compressors'

Crushing'&'grinding'machines'Surveying'instruments'

Weighing'machinery' Sacks'&'bags,'packing'of'jute'or'other'bast'fibres'

KnoHed'neIng'of'natural'materials'Twine,'ropes'&'cables'of'other'materials'

Brooms'&'brushes'of'vegetable'materials' MaHs'&'screens'from'

vegetable'material'Binder'or'baler'twine,'of'sisal'or'agave'

Jute'and'other'tex<le'bast'fibres'

Reservoirs,'ranks'and'vats'of'iron'and'steel'Tank,'cask'&'

containers'

Sheet/film'of''polymers'of'ethylene'

Limestone'flux'

Lighting fittings using LED lamp'

Liquid'dialec<c'transformers'

AC generators: 375-750 kVA'

AC generators: < 750 kVA'

AC generators: 75 -375 kVA'

Methyl alcohol'

Anhydrous ammonia'

Railway/tramway switch crossing material'

Instruments to measure or detect ionising radiations'

Buses, except diesel powered'Self-propelled

railway cars, external electric power'

Electric space heating apparatus'

Optical devices, appliances and instruments'

Steam and vapour turbines'

Hydraulic turbines'

Machines for balancing mechanical parts'

Generating sets'

Gas turbine engines'Thermometers, liquid filled'

Ion exchangers'

Parts of hydraulic power engines'

Vacuum pumps'

Chromatographs'Catalysts'

Discharge lamps, other than ultra-violet lamps'

Spectrometers'

Electrical measurement instruments'

Parts of electrical measurement instruments'

Cathode-ray oscilloscapes'

Prisms, mirrors, optical elements'

Photosensitive/photovoltaic/LED semiconductor devices'

Primary cells, primary batteries'

UV disinfection ozonisers'

Parts of UV disinfection ozonisers'

Parts and accessories of optical appliances'

Thermometers, except liquid filled'

Hydrometer'

Railway driving bogies'

Complexity'

High'

Low'

Figure 10: The Green Product Space

40

Germany( South(Korea(

Saudi(Arabia( Uganda(

RCA(>(1(

RCA(<(1(

A" B"

C" D"

Figure 11: Competitive green product spaces for a selection of countries

A.7 Robustness checks for regression results

There is not enough within country variation in the GCI and GCP for the relativelyshort period covered by our dataset to run a country-fixed-effect panel regression.Instead, we present additional regression analyses for different years covered byour dataset.

41

A.7.1 GCI and Environmental Patents

Table 13: Robustness tests for the relationship between GCI and LogEnv. Patents over different years

2010 2005 2000GCI 1.144*** 1.034*** 1.205***

(0.201) (0.213) (0.177)ECI 0.956*** 1.147*** 0.782***

(0.307) (0.273) (0.189)Log GDP/Cap 0.208 0.003 0.111

(0.150) (0.127) (0.102)Intercept 0.632 2.338** 1.089

(1.237) (1.067) (0.798)Observations 122 122 122Adjusted R2 0.752 0.741 0.755

Robust standard errors in parenthesis.Significance levels: * p<0.1, ** p<0.05, *** p<0.01The Env. Patent variable can be found in the OECD Statistics database under Environment– Innovation in environment-related tech – Technology Development (Family Size: one orgreater; Technology Domain: Selected Environment-Related Technologies). Available at:http://stats.oecd.org/

A.7.2 GCI and CO2 Emissions

Table 14: Robustness tests for the relationship between GCI and LogCO2/cap emissions over different years

2010 2005 2000GCI −0.171* −0.333*** −0.2765***

(0.097) (0.100) (0.103)ECI 0.048 0.377** 0.398**

(0.158) (0.152) (0.153)Log GDP/Cap 0.923*** 0.767*** 0.746***

(0.091) (0.080) (0.087)Intercept −7.086*** −5.432*** −5.017***

(0.807) (0.679) (0.708)Observations 122 122 122Adjusted R2 0.756 0.755 0.715

Robust standard errors in parenthesis.Significance levels: * p<0.1, ** p<0.05, *** p<0.01CO2/cap (metric tons per capita) is sourced from https://data.worldbank.org/indicator/

EN.ATM.CO2E.PC

42

A.7.3 GCI and Environmental Policy Stringency

Table 15: Robustness tests for the relationship between the GCI andEnvironmental Policy Stringency over different years

2010 2005 2000GCI 0.085* 0.100** 0.099***

(0.049) (0.047) (0.028)ECI −0.079 −0.096 −0.091

(0.085) (0.083) (0.057)Log GDP/Cap 0.236*** 0.218*** 0.155***

(0.055) (0.038) (0.031)Intercept −1.154** −1.122*** −0.704***

(0.538) (0.331) (0.242)Observations 31 31 31Adjusted R2 0.527 0.556 0.647

Robust standard errors in parenthesis.Significance levels: * p<0.1, ** p<0.05, *** p<0.01Environmental Policy Stringency (EPS) data is sourced from http://stats.oecd.org/

43

A.7.4 GCP - 10 year average predictions

Table 16: Green Complexity Potential Regression Analysis (10 year averages)

∆ GCI ∆ #Green exported ∆ Green export(t+ δ) products (t+ δ) trade ratio (t+ δ)

Log GCP(t) 0.132*** 5.223*** 0.009***(0.028) (1.194) (0.003)

Log GDP/Cap(t) 0.004 0.018 0.001(0.017) (0.757) (0.001)

ECI(t) −0.124*** −6.374*** −0.005*(0.032) (1.508) (0.003)

GCI(t) −0.032(0.037)

#Green exported products(t) 0.086**(0.040)

Green export trade ratio(t) −0.012(0.130)

Intercept 0.381* 17.989** 0.025*(0.170) (7.449) (0.013)

Observations 2440 2440 2440Adjusted R2 0.211 0.251 0.152

Robust standard errors in parenthesis.Significance levels: * p<0.1, ** p<0.05, *** p<0.01t relates to country averaged values over years 1995-2004 and t + δ relates to country averaged values over years2005-2014.

44

A.7.5 Green Stimulus - Total Spend

Table 17: Green Stimulus Total Spend

∆ GCI ∆ #Green exported ∆ Green export(t+ δ) products (t+ δ) trade ratio (t+ δ)

Green Stimulus ($US Bn) 0.0028*** 0.1276*** 0.0001***(0.0004) (0.0179) (0.0000)

Log GDP/Cap(t) 0.0000 0.0000 0.0000(0.0000) (0.0001) (0.0000)

GCI(t) −0.0042(0.0153)

Green exported products(t) 0.0171(0.0184)

Green export trade ratio(t) 0.0605(0.0479)

Intercept −0.0054 −0.3881 −0.0080*(0.0368) (1.7720) (0.002)

Observations 19 19 19Adjusted R2 0.586 0.628 0.236

Robust standard errors in parenthesis.Significance levels: * p<0.1, ** p<0.05, *** p<0.01t relates to the year 2008 and (t+ δ) relates to the year 2011.Green Stimulus Data is from Table 1 in Barbier et al. (2010), and relates to low carbon support for renewableenergy, carbon capture and sequestration, energy efficiency, public transport and rail, and improving electricalgrid transmission.#Green exported products refers to the number of green exports in which the country has RCA > 1.

A.8 GCI robustness tests using alternative complexity mea-sures

An alternative approach for estimating the complexity of productive capabilitiesassociated with countries and exported products has also being proposed by Tac-chella et al. (2012). This methodology uses the same binary Mcp matrix con-structed on the basis of countries’ RCA’s, as defined in section 3.2. However,Tacchella et al. (2012) introduce a different formulation for arriving at a country-specific estimate (called Fitness ) and a product-specific estimate (called Com-plexity).

The measures are calculated as the fixed-point solution of the non-linear iterativemapping given by

45

F

(N)c =

∑pMcpQ

(N−1)p

Q(N)p =

1∑cMcp

1

F(N−1)c

→

F(N)c =

F(N)c

1C

∑c F

(N)c

Q(N)p =

Q(N)p

1P

∑p Q

(N)p

, (10)

where F(N)c and Q

(N)p are the N th iterations for the Fitness of country c and Com-

plexity of the product p respectively, and P is the number of products. The initialconditions are given by vectors of 1’s (i.e. F

(0)c = 1∀p and Q

(0)p = 1∀c), and at

each iteration, the intermediate variables F(N)c and Q

(N)p are calculated and then

normalized by the average values.

As shown in Cristelli et al. (2015, 2017), the Fitness measure appears useful forpredicting the growth of countries falling into a particular region in the Fitness ×GDP per capita plane.

Here, we compare the GCI regression results using different product complexityformulations. We use GCI(HH) to denote the GCI calculated on the basis of theHausmann et al. (2014) Product Complexity Index (as specified in equation 6) andGCI(Tacch) to denote the GCI calculated on the basis of the alternative ProductComplexity measure proposed by Tacchella et al. (2012).

In Table 18 we show that the relationship between environmental patents, carbonemissions and environmental policy stringency are very similar for both GCI(HH)and GCI(Tacch). This suggests that the GCI is robust to the choice of productcomplexity measure.

46

Table 18: Comparison of GCI regression results using alternative complexity measures

Log Env. Patents Log CO2/cap Log EPS

GCI(HH) 1.551*** −0.168** 0.070***(0.174) (0.080) (0.022)

GCI(Tacch) 1.584*** −0.180** 0.056**(0.169) (0.077) (0.021)

Log GDP/Cap 0.524*** 0.532*** 0.946*** 0.948*** 0.168*** 0.175***(0.109) (0.104) (0.060) (0.059) (0.021) (0.022)

Intercept −1.913*** −1.975** −6.990*** −7.010*** −0.757*** −0.805***(0.908) (0.864) (0.521) (0.510) (0.204) (0.215)

Observations 1220 1220 2318 2318 558 558Adjusted R2 0.727 0.747 0.760 0.761 0.726 0.708

Robust standard errors in parenthesis.Significance levels: * p < 0.1, ** p < 0.05, *** p < 0.01GCI (HH) refers to the GCI calculated using the Hausmann et al. (2014) Product Complexity Index and GCI (Tacch)refers to the GCI calculated using the Tacchella et al. (2012) Product Complexity measure Environmental patents datacovers 2000 and 2005-2013, available from http://stats.oecd.org/. CO2 (metric tons per capita) data covers 1995-2013, available from https://data.worldbank.org/indicator/EN.ATM.CO2E.PC. Environmental Policy Stringency(EPS) data covers 1995-2012, available from http://stats.oecd.org/. For all regressions, we take country averagesover all available time periods.

47

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